Enhancing Intrusion Detection System Using Osprey Optimization Algorithm with Ensemble Learning Model
Enhancing Intrusion Detection System Using Osprey Optimization Algorithm with Ensemble Learning Model |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-10 |
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| Year of Publication : 2024 | ||
| Author : Sneha Bohra, Amit Gaikwad |
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| DOI : 10.14445/22315381/IJETT-V72I10P119 | ||
How to Cite?
Sneha Bohra, Amit Gaikwad, "Enhancing Intrusion Detection System Using Osprey Optimization Algorithm with Ensemble Learning Model," International Journal of Engineering Trends and Technology, vol. 72, no. 10, pp. 191-203, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I10P119
Abstract
Traditional Recommendation Systems suffer from Concept Drift- a phenomenon that assumes that user preferences are static over time. To address this issue, there is a need for a recommendation algorithm that takes into account time-sensitive shifts in user preferences and offers relevant recommendations. This research work proposes an Ensemble-based Hybrid Recommendation System which incorporates temporal variations in user interests. The proposed system combines distinct algorithms such as Popularity, Clustering, Collaborative Matrix Factorization and Singular Value Decomposition (SVD). The movie recommendations obtained from these individual models are then combined and classified using Artificial Neural Network (ANN). User feedback on the presented batch of recommendations is then recorded, which contributes to the calculation of the relevancy factor for each batch of recommendations. Finally, the user is provided with relevant movie recommendations. In case of a lower relevancy factor, the recommendations are reclassified. The objective of the proposed system is to provide diverse recommendations to the user depending on his time-sensitive preferences. The novelty of the proposed research is the integration of the popular recommendation strategies and the incorporation of the user feedback mechanism on the presented recommendations. The proposed system is implemented on the standard movie dataset Movielens-25m and is evaluated using statistical performance metrics like RMSE and MAE. The experiments show that the integration of an Artificial Neural Network as a classifier to the Ensemble- Hybrid Recommendation Model demonstrates promising results in terms of providing relevant recommendations with 0.56 and 0.43 as RMSE and MAE values. The work illustrates the improvement in recommendation quality and increased adaptability to varying user preferences.
Keywords
Movie recommendation system, Collaborative filtering, Content-based filtering, Hybrid approach, Singular Value Decomposition (SVD), Neural Network (NN).
References
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